The current disconnection between access to increasing amounts of data about urbanization, health, and other global changes and the conflicting meanings and values of that data has created uncertainty and reduced the ability of people to act upon available information which they do not necessarily understand. We see a disconnection between increasing data availability and data processing capability and capacity. In response to this disconnection, modeling has been attributed an important role in international and national research programs in order to predict the future based on past and recent trends. Predictive models are often data heavy and founded on assumptions which are difficult to verify, especially regarding urban health issues in specific contexts. Producing large volumes of data warrants debate about what data are prerequisites for better understanding human health in changing urban environments. Another concern is how data and information can be used to apply knowledge. Making sense of empirical knowledge requires a new transdisciplinary knowledge domain created by a commitment to convergence between researchers in multiple academic disciplines and other actors and institutions in cities. Disciplinary-based researchers are no longer the sole producers of empirical knowledge. Today, diverse kinds of knowledge are becoming an emergent product of multiple societal stakeholders acting collectively to address challenges that impact on their habitat, their livelihood, and their health. Insights from complexity science also require a fundamental rethinking of the role and responsibility of human agency while admitting rather than denying complexity and radical uncertainty.
* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.